Table of Contents

• How to evaluate an analytical database (the criteria that matter)
• The comparison table
• Option-by-option analysis

  • DuckDB — the rival more people should consider first
  • StarRocks — the serious alternative if your data is normalized
  • Apache Druid — powerful and operationally expensive
  • Apache Pinot — the king of user-facing analytics
  • TimescaleDB (TigerData) — the alternative that doesn't force you to migrate
  • BigQuery and Snowflake — zero operations, variable bill
  • Honorable mention: Apache Doris and Tinybird
• Verdict: what to choose by your situation
• What I learned choosing wrong
• Conclusion
• Frequently asked questions

  • What's the best alternative to ClickHouse?
  • ClickHouse or DuckDB?
  • ClickHouse or BigQuery?
  • Is StarRocks better than ClickHouse?
  • Is Apache Druid still worth it in 2026?

How to evaluate an analytical database (the criteria that matter)

Before the table, it's worth fixing the axes. I've seen too many people choose by benchmark and regret it because of operations.

Real operational cost. Not the license price —almost all are open source— but how many hours a month you'll spend keeping it alive. This is the criterion that weighs most when you work alone, and the one that least appears in comparisons.

Deployment model. A binary? A cluster of six process types? Cloud-managed only? Determines whether you can start on a 20 € VPS or need Kubernetes.

Ingestion latency. How long until an event is queryable? Seconds, minutes or hours. Only matters if you genuinely need real time; a lot of people think they need it and don't.

Concurrency. How many simultaneous queries does it handle? Critical if you build analytics inside your product for your customers; irrelevant if it's an internal dashboard that three people look at.

Cost model at scale. Pay-per-consumption (BigQuery, Snowflake) vs. fixed server (self-hosted ClickHouse). The first is great when usage is low and terrifying when someone leaves a dashboard auto-refreshing.

Ecosystem and driver maturity. If your stack is Python or Node, is there an official client? Does it connect to Metabase or Grafana without pain?

The comparison table

Read it knowing that the cells are simplifications and that the detail is in the sections that follow.

	ClickHouse	DuckDB	StarRocks	Apache Druid	Apache Pinot	TimescaleDB	BigQuery / Snowflake
Type	Distributed columnar OLAP	Embedded (in-process) OLAP	Columnar MPP OLAP	Real-time OLAP	Real-time OLAP	PostgreSQL extension	Managed cloud warehouse
License	Apache 2.0	MIT	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0 / TSL	Proprietary
Minimum deployment	1 binary	1 library	Frontend + Backend nodes	6 process types + ZooKeeper	Controller, Broker, Server + ZooKeeper	Postgres + extension	SaaS only
Operational difficulty	Low—medium	None	Medium	High	High	Very low	None (you pay for it)
Aggregate query performance	Excellent	Excellent (single-node)	Excellent	Very good	Very good	Good	Good—very good
Complex JOINs	Very good	Excellent	Excellent (its strength)	Limited	Limited (improving)	Excellent (it's Postgres)	Excellent
Streaming ingestion	Good (Kafka engine, MVs)	N/A	Good	Excellent	Excellent (its strength)	Good	Medium (batch-first)
Ingest—query latency	Seconds	N/A	Seconds	Sub-second	Sub-second	Immediate	Minutes
High concurrency	Very good	Low (one process)	Very good	Good	Excellent	Medium	Good but expensive
UPDATE / DELETE	Limited (lightweight mutations)	Yes	Yes (its strength)	Very limited	Very limited	Complete	Yes (costly)
SQL dialect	Own, heavily extended	Postgres-compatible	MySQL-compatible	Own SQL	Own SQL	Pure PostgreSQL	Standard SQL
Practical scale	TB—PB	GB—hundreds of GB	TB—PB	TB—PB	TB—PB	GB—tens of TB	PB
Cost model	Fixed server	Free (runs in your process)	Fixed server	Fixed server (expensive)	Fixed server (expensive)	Fixed server	Per consumption
Fit for a solo maker	✅ Yes	✅ Yes	⚠️ With effort	❌ No	❌ No	✅ Yes	⚠️ Bill risk

Option-by-option analysis

DuckDB — the rival more people should consider first

DuckDB is an in-process analytical database: there's no server, it runs inside your application as a library, just like SQLite but for analytics. pip install duckdb and you have a vectorized columnar engine.

Where it clearly wins: absolute simplicity. Zero operations, zero ports, zero backups to configure. It queries Parquet and CSV directly, integrates with pandas and Polars natively, and its SQL dialect is PostgreSQL-compatible, so you don't learn new syntax. For local data analysis, notebooks, ETL, or the analytical backend of a small app, it's hard to beat.

Where it loses: it's fundamentally a single process. It doesn't serve data to tens of concurrent users, it doesn't scale horizontally, and it isn't designed to be a shared service receiving constant writes from several sources. MotherDuck exists as a cloud layer if you need that, but then you're already in another model.

My opinion: if your dataset fits on a disk and your queries are launched by your backend or by you from a notebook, start with DuckDB, not ClickHouse. It's the recommendation people resist most and the one that's most often right. Complexity is added when it hurts not to have it.

The practical frontier is where you need a service instead of a library: multiple writers, multiple concurrent readers, data that doesn't comfortably fit on one machine, years of retention. There you cross over to ClickHouse.

StarRocks — the serious alternative if your data is normalized

StarRocks is a columnar MPP database that shares a lot of philosophy with ClickHouse but differs on two points that matter.

Where it wins: multi-table JOINs and real-time updates. StarRocks was designed from the start assuming you'd query a normalized star schema, with a mature cost-based optimizer to reorder joins. If you come from a traditional data warehouse with fact and dimension tables and don't want to denormalize everything, it fits better. It's also MySQL-protocol compatible, which means any MySQL client connects without a special driver.

Where it loses: a smaller ecosystem and community than ClickHouse. Fewer ready integrations, fewer answers on Stack Overflow when something breaks at 2 a.m., fewer engineers who already know it. And the minimum install involves two node types (frontend and backend), not a binary.

My opinion: it's the most technically comparable alternative to ClickHouse. If your workload is "warehouse with lots of joins and frequent updates", evaluate it seriously. If your workload is "torrent of events that I aggregate by time", ClickHouse is still simpler and faster to get running.

Apache Druid — powerful and operationally expensive

Druid has been doing real-time time-series analytics at scale for years, and does it well. Its architecture is finely tuned for ingestion from Kafka with immediate data availability.

Where it wins: streaming ingestion with sub-second latency and a clear separation between "hot" data (recent, in memory) and "cold" (historical, in deep storage). For a large-scale monitoring use case with long retention, the design makes sense.

Where it loses: operational complexity. Druid needs Coordinator, Overlord, Broker, Router, Historical and MiddleManager, plus ZooKeeper for coordination and a metadata store. That's six process types plus two external dependencies. It's a cluster that needs dedicated people. Plus JOINs remain its weak point, and the data model is less flexible.

My opinion: for a maker or a small team, Druid is directly unviable. Not because it's bad technology —it isn't— but because its operational cost isn't amortized except at very large scales. It's telling that several well-known companies (Lyft among them) have migrated from Druid to ClickHouse citing cost reduction and architecture simplification.

Apache Pinot — the king of user-facing analytics

Pinot was born at LinkedIn to serve "who viewed your profile" to hundreds of millions of users. That DNA shows: it's optimized for millisecond latencies with very high concurrency.

Where it wins: if you build an analytical feature inside your product that thousands of users will query simultaneously with a strict latency SLA, Pinot has the architecture most specifically designed for that. Star indexes, inverted indexes, tuned partitioning for that pattern.

Where it loses: like Druid, high operational complexity (Controller, Broker, Server, plus ZooKeeper). Less flexible for exploratory ad-hoc queries: it shines when you know your query patterns in advance and can index for them, not when you want to explore.

My opinion: excellent tool for a very specific problem that almost no small project has. If you're not serving analytics to more than a thousand concurrent users, ClickHouse gives you 90% of the result with 20% of the complexity.

TimescaleDB (TigerData) — the alternative that doesn't force you to migrate

TimescaleDB is a PostgreSQL extension for time series. In June 2025 the company renamed itself to TigerData, although the open source extension keeps the name TimescaleDB.

Where it wins: it's PostgreSQL. All your knowledge, your drivers, your tools, your backups, your ORM, your JOINs with application tables: everything keeps working. It adds "hypertables" (automatic time-based partitioning), continuous aggregates and columnar compression. And since it's Postgres, you get full ACID transactions and drama-free UPDATE/DELETE.

Where it loses: performance on aggregations over very large volumes isn't on ClickHouse's level. It's still Postgres underneath, with the limitations of a row-oriented engine patched with columnar compression. At tens of terabytes, the difference is noticeable.

My opinion: undervalued by people chasing the new. If you already use Postgres, your data is time-series, and your problem is tens or hundreds of gigabytes rather than terabytes, TimescaleDB is probably the best cost-benefit decision you can make. You migrate with an extension instead of a new service. The complexity you don't add is complexity you don't maintain.

BigQuery and Snowflake — zero operations, variable bill

Managed cloud warehouses eliminate the operational problem entirely. There's no server, no updates, no backups. You write SQL and pay.

Where they win: practically unlimited scale without thinking about infrastructure, deep integration with the rest of the cloud ecosystem, and huge maturity in data governance, permissions and compliance. If you're a company with budget and want the team focused on the business, it's a defensible decision.

Where they lose: the pricing model. You pay by data scanned (BigQuery) or by compute time (Snowflake), and both models punish exactly the pattern of interactive analytics: many small, frequent queries. A dashboard with auto-refresh every 30 seconds can generate a disproportionate bill. Plus query latency rarely drops below hundreds of milliseconds to seconds, which makes them poorly suited for analytics embedded in a product.

My opinion: for a bootstrapped project, the variable-bill risk is a business risk, not a technical one. I've seen projects where the BigQuery cost exceeded that of all the rest of the infrastructure combined. A dedicated server with ClickHouse has a known and flat cost, and that's worth a lot when you bill little. The reasonable exception: if your data already lives in Google Cloud and your query volume is low and sporadic, BigQuery may be cheaper than maintaining a server.

Honorable mention: Apache Doris and Tinybird

Apache Doris is very similar to StarRocks (they share origin) and deserves a look for the same reasons. Tinybird is a managed layer built on ClickHouse that adds APIs over your queries — interesting if you want ClickHouse without operating it and with a product layer on top.

Verdict: what to choose by your situation

I translate all of the above into concrete recommendations.

You're exploring data in a notebook or have less than 50 GB — DuckDB. No discussion. Zero operations, excellent performance, familiar SQL.

You already use PostgreSQL, your data is time-series, and we're talking tens of GB — TimescaleDB. You add an extension instead of a service. The most boring option and probably the correct one.

You have a torrent of events, want self-hosting, and work alone or in a small team — ClickHouse. The optimum point between performance and operational complexity. A binary on a VPS handles more than most projects will ever need.

You build analytics inside your product for thousands of concurrent users — ClickHouse first, Pinot if you fall short. Start with the simple; migrate if the SLA truly demands it.

Your schema is a normalized warehouse with lots of JOINs and updates — StarRocks. Its join optimizer is its real advantage over ClickHouse.

You have budget, a team, and want zero infrastructure — BigQuery or Snowflake. Set cost alerts from day one.

You need sub-second ingestion at massive scale and have dedicated SREs — Druid or Pinot. If you don't have dedicated SREs, this line isn't for you.

What I learned choosing wrong

Twice I've chosen the most powerful tool instead of the right one, and both times I paid for it.

The first time I set up a cluster for a project that had 12 million rows. Twelve million. DuckDB would have answered those queries in milliseconds from a single file. I spent a weekend configuring something that solved a problem I didn't have.

The second time I chose by benchmark. Database benchmarks measure query latency in lab conditions; they don't measure how many hours you'll spend debugging ingestion, or how much it hurts that the community is so small your error doesn't appear anywhere. The real cost of a database is the time it steals from you, not the milliseconds it saves you.

My current criterion, in order: first choose the simplest option that solves your problem today; second, verify it has a clear exit path (can you export to Parquet?); third, and only then, look at performance. If the simplest option doesn't hold up within a year, you'll migrate then — and by then you'll know much better what you need.

Conclusion

There's no "best OLAP database". There's a best one for your volume, your team and your budget, and for most small projects that answer is DuckDB or ClickHouse, not the more sophisticated options.

ClickHouse occupies a very particular space in this landscape: distributed-system performance with monolithic-application operational complexity. That combination is rare, and it's the reason it remains my default option when the project has outgrown what DuckDB can give.

If you've already decided on ClickHouse, the next step is to get real value out of it: the advanced ClickHouse guide covers materialized views, projections, codecs and everything that separates a mediocre ClickHouse from a fast one. And if you don't have it installed yet, start with what ClickHouse is and how to install it.

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Frequently asked questions

What's the best alternative to ClickHouse?

It depends on the case. DuckDB for datasets that fit on a machine, StarRocks if you need complex JOINs and frequent updates, TimescaleDB if you already use PostgreSQL, and Apache Pinot if your priority is minimum latency with very high concurrency.

ClickHouse or DuckDB?

DuckDB if your data fits on a disk and queries are launched by a single process. ClickHouse when you need a shared service, concurrent writes from several sources or to scale beyond one machine.

ClickHouse or BigQuery?

ClickHouse if you want flat and predictable cost and millisecond latencies; BigQuery if you prefer zero operations, your query volume is low and sporadic, and your data already lives in Google Cloud.

Is StarRocks better than ClickHouse?

On multi-table JOINs and real-time updates, generally yes. On deployment simplicity, community size and the integration ecosystem, ClickHouse keeps the advantage.

Is Apache Druid still worth it in 2026?

At very large scales with dedicated infrastructure teams, yes. For small and medium projects, its operational cost is rarely justified against ClickHouse.